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PaddleSlim
未验证 提交 044909e7 编写于 作者: W whs 提交者： GitHub
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Remove fluid API (#1567)

上级 87cfc2e6
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Showing with 179 addition and 265 deletion
ce_tests/dygraph/quant/src/save_quant_model.py
浏览文件 @ 044909e7
......@@ -24,7 +24,7 @@ import time
import paddle
from paddle.fluid.framework import IrGraph
from paddle.fluid.contrib.slim.quantization import Quant2Int8MkldnnPass
from paddle.fluid import core
from paddle.framework import core
paddle.enable_static()
......
demo/darts/model.py
浏览文件 @ 044909e7
......@@ -74,7 +74,7 @@ class Classifier(paddle.nn.Layer):
def forward(self, x):
x = self.pool2d(x)
x = fluid.layers.squeeze(x, axes=[2, 3])
x = paddle.squeeze(x, axes=[2, 3])
out = self.fc(x)
return out
......@@ -85,7 +85,7 @@ def drop_path(x, drop_prob):
mask = 1 - np.random.binomial(
1, drop_prob, size=[x.shape[0]]).astype(np.float32)
mask = to_variable(mask)
x = fluid.layers.elementwise_mul(x / keep_prob, mask, axis=0)
x = paddle.multiply(x / keep_prob, mask)
return x
......@@ -122,7 +122,7 @@ class Cell(paddle.nn.Layer):
op = OPS[op_name](c_curr, stride, True)
ops += [op]
edge_index += 1
self._ops = fluid.dygraph.LayerList(ops)
self._ops = paddle.nn.LayerList(ops)
self._indices = indices
def forward(self, s0, s1, drop_prob, training):
......@@ -206,7 +206,7 @@ class NetworkCIFAR(paddle.nn.Layer):
c_prev_prev, c_prev = c_prev, cell._multiplier * c_curr
if i == 2 * layers // 3:
c_to_auxiliary = c_prev
self.cells = fluid.dygraph.LayerList(cells)
self.cells = paddle.nn.LayerList(cells)
if auxiliary:
self.auxiliary_head = AuxiliaryHeadCIFAR(c_to_auxiliary,
......@@ -288,7 +288,7 @@ class NetworkImageNet(paddle.nn.Layer):
c_prev_prev, c_prev = c_prev, cell._multiplier * c_curr
if i == 2 * layers // 3:
c_to_auxiliary = c_prev
self.cells = fluid.dygraph.LayerList(cells)
self.cells = paddle.nn.LayerList(cells)
if auxiliary:
self.auxiliary_head = AuxiliaryHeadImageNet(c_to_auxiliary,
......
demo/darts/model_search.py
浏览文件 @ 044909e7
......@@ -16,12 +16,14 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
import paddle.fluid as fluid
from paddle.nn.initializer import Normal, KaimingUniform, Constant
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear
from paddle.nn import Conv2D, Pool2D, BatchNorm, Linear
from paddle.fluid.dygraph.base import to_variable
from genotypes import PRIMITIVES
from operations import *
import paddleslim
def channel_shuffle(x, groups):
......@@ -31,7 +33,7 @@ def channel_shuffle(x, groups):
# reshape
x = paddle.reshape(x,
[batchsize, groups, channels_per_group, height, width])
x = fluid.layers.transpose(x, [0, 2, 1, 3, 4])
x = paddle.transpose(x, [0, 2, 1, 3, 4])
# flatten
x = paddle.reshape(x, [batchsize, num_channels, height, width])
......@@ -59,12 +61,12 @@ class MixedOp(paddle.nn.Layer):
trainable=False)
BN = BatchNorm(
c_cur // self._k, param_attr=gama, bias_attr=beta)
op = fluid.dygraph.Sequential(op, BN)
op = paddle.nn.Sequential(op, BN)
ops.append(op)
self._ops = fluid.dygraph.LayerList(ops)
self._ops = paddle.nn.LayerList(ops)
def forward(self, x, weights):
return fluid.layers.sums(
return paddle.add_n(
[weights[i] * op(x) for i, op in enumerate(self._ops)])
......@@ -89,7 +91,7 @@ class Cell(paddle.nn.Layer):
stride = 2 if reduction and j < 2 else 1
op = MixedOp(c_cur, stride, method)
ops.append(op)
self._ops = fluid.dygraph.LayerList(ops)
self._ops = paddle.nn.LayerList(ops)
def forward(self, s0, s1, weights, weights2=None):
s0 = self.preprocess0(s0)
......@@ -98,7 +100,7 @@ class Cell(paddle.nn.Layer):
states = [s0, s1]
offset = 0
for i in range(self._steps):
s = fluid.layers.sums([
s = paddle.add_n([
self._ops[offset + j](h, weights[offset + j])
for j, h in enumerate(states)
])
......@@ -127,7 +129,7 @@ class Network(paddle.nn.Layer):
self._method = method
c_cur = stem_multiplier * c_in
self.stem = fluid.dygraph.Sequential(
self.stem = paddle.nn.Sequential(
Conv2D(
num_channels=3,
num_filters=c_cur,
......@@ -154,7 +156,7 @@ class Network(paddle.nn.Layer):
reduction_prev = reduction
cells.append(cell)
c_prev_prev, c_prev = c_prev, multiplier * c_cur
self.cells = fluid.dygraph.LayerList(cells)
self.cells = paddle.nn.LayerList(cells)
self.global_pooling = Pool2D(pool_type='avg', global_pooling=True)
self.classifier = Linear(
input_dim=c_prev,
......@@ -174,13 +176,13 @@ class Network(paddle.nn.Layer):
weights = paddle.nn.functional.softmax(self.alphas_normal)
s0, s1 = s1, cell(s0, s1, weights, weights2)
out = self.global_pooling(s1)
out = fluid.layers.squeeze(out, axes=[2, 3])
out = paddle.squeeze(out, axes=[2, 3])
logits = self.classifier(out)
return logits
def _loss(self, input, target):
logits = self(input)
loss = fluid.layers.reduce_mean(
loss = paddle.mean(
paddle.nn.functional.softmax_with_cross_entropy(logits, target))
return loss
......
demo/darts/operations.py
浏览文件 @ 044909e7
......@@ -71,8 +71,8 @@ class Zero(paddle.nn.Layer):
def forward(self, x):
pooled = self.pool(x)
x = fluid.layers.zeros_like(
x) if self.stride == 1 else fluid.layers.zeros_like(pooled)
x = paddle.zeros_like(x) if self.stride == 1 else paddle.zeros_like(
pooled)
return x
......
demo/darts/search.py
浏览文件 @ 044909e7
......@@ -63,7 +63,8 @@ def main(args):
elif not args.use_data_parallel:
place = paddle.CUDAPlace(0)
else:
place = paddle.CUDAPlace(fluid.dygraph.parallel.Env().dev_id)
place = paddle.CUDAPlace(paddle.distributed.parallel.ParallelEnv()
.dev_id)
train_reader, valid_reader = reader.train_search(
batch_size=args.batch_size,
......
demo/darts/train.py
浏览文件 @ 044909e7
......@@ -79,10 +79,10 @@ def train(model, train_reader, optimizer, epoch, drop_path_prob, args):
prec1 = paddle.static.accuracy(input=logits, label=label, k=1)
prec5 = paddle.static.accuracy(input=logits, label=label, k=5)
loss = fluid.layers.reduce_mean(
loss = paddle.mean(
paddle.nn.functional.softmax_with_cross_entropy(logits, label))
if args.auxiliary:
loss_aux = fluid.layers.reduce_mean(
loss_aux = paddle.mean(
paddle.nn.functional.softmax_with_cross_entropy(logits_aux,
label))
loss = loss + args.auxiliary_weight * loss_aux
......@@ -122,7 +122,7 @@ def valid(model, valid_reader, epoch, args):
logits, _ = model(image, 0, False)
prec1 = paddle.static.accuracy(input=logits, label=label, k=1)
prec5 = paddle.static.accuracy(input=logits, label=label, k=5)
loss = fluid.layers.reduce_mean(
loss = paddle.mean(
paddle.nn.functional.softmax_with_cross_entropy(logits, label))
n = image.shape[0]
......@@ -137,7 +137,7 @@ def valid(model, valid_reader, epoch, args):
def main(args):
place = paddle.CUDAPlace(fluid.dygraph.parallel.Env().dev_id) \
place = paddle.CUDAPlace(paddle.distributed.parallel.ParallelEnv().dev_id) \
if args.use_data_parallel else paddle.CUDAPlace(0)
with fluid.dygraph.guard(place):
......@@ -152,7 +152,7 @@ def main(args):
logger.info("param size = {:.6f}MB".format(
count_parameters_in_MB(model.parameters())))
device_num = fluid.dygraph.parallel.Env().nranks
device_num = paddle.distributed.parallel.ParallelEnv().nranks
step_per_epoch = int(args.trainset_num / (args.batch_size * device_num))
learning_rate = fluid.dygraph.CosineDecay(args.learning_rate,
step_per_epoch, args.epochs)
......@@ -200,7 +200,7 @@ def main(args):
save_parameters = (not args.use_data_parallel) or (
args.use_data_parallel and
fluid.dygraph.parallel.Env().local_rank == 0)
paddle.distributed.parallel.ParallelEnv().local_rank == 0)
best_acc = 0
for epoch in range(args.epochs):
drop_path_prob = args.drop_path_prob * epoch / args.epochs
......
demo/darts/train_imagenet.py
浏览文件 @ 044909e7
......@@ -69,9 +69,9 @@ add_arg('use_data_parallel', ast.literal_eval, False, "The flag indicating whet
def cross_entropy_label_smooth(preds, targets, epsilon):
preds = paddle.nn.functional.softmax(preds)
targets_one_hot = fluid.one_hot(input=targets, depth=args.class_num)
targets_smooth = fluid.layers.label_smooth(
targets_smooth = paddle.nn.functional.label_smooth(
targets_one_hot, epsilon=epsilon, dtype="float32")
loss = fluid.layers.cross_entropy(
loss = paddle.nn.functional.cross_entropy(
input=preds, label=targets_smooth, soft_label=True)
return loss
......@@ -91,11 +91,11 @@ def train(model, train_reader, optimizer, epoch, args):
prec1 = paddle.static.accuracy(input=logits, label=label, k=1)
prec5 = paddle.static.accuracy(input=logits, label=label, k=5)
loss = fluid.layers.reduce_mean(
loss = paddle.mean(
cross_entropy_label_smooth(logits, label, args.label_smooth))
if args.auxiliary:
loss_aux = fluid.layers.reduce_mean(
loss_aux = paddle.mean(
cross_entropy_label_smooth(logits_aux, label,
args.label_smooth))
loss = loss + args.auxiliary_weight * loss_aux
......@@ -135,7 +135,7 @@ def valid(model, valid_reader, epoch, args):
logits, _ = model(image, False)
prec1 = paddle.static.accuracy(input=logits, label=label, k=1)
prec5 = paddle.static.accuracy(input=logits, label=label, k=5)
loss = fluid.layers.reduce_mean(
loss = paddle.mean(
cross_entropy_label_smooth(logits, label, args.label_smooth))
n = image.shape[0]
......@@ -150,7 +150,7 @@ def valid(model, valid_reader, epoch, args):
def main(args):
place = paddle.CUDAPlace(fluid.dygraph.parallel.Env().dev_id) \
place = paddle.CUDAPlace(paddle.distributed.parallel.ParallelEnv().dev_id) \
if args.use_data_parallel else paddle.CUDAPlace(0)
with fluid.dygraph.guard(place):
......@@ -165,7 +165,7 @@ def main(args):
logger.info("param size = {:.6f}MB".format(
count_parameters_in_MB(model.parameters())))
device_num = fluid.dygraph.parallel.Env().nranks
device_num = paddle.distributed.parallel.ParallelEnv().nranks
step_per_epoch = int(args.trainset_num / (args.batch_size * device_num))
learning_rate = fluid.dygraph.ExponentialDecay(
args.learning_rate, step_per_epoch, args.decay_rate, staircase=True)
......@@ -209,7 +209,7 @@ def main(args):
save_parameters = (not args.use_data_parallel) or (
args.use_data_parallel and
fluid.dygraph.parallel.Env().local_rank == 0)
paddle.distributed.parallel.ParallelEnv().local_rank == 0)
best_top1 = 0
for epoch in range(args.epochs):
logger.info('Epoch {}, lr {:.6f}'.format(epoch, optimizer.get_lr()))
......
demo/distillation/distill.py
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......@@ -13,7 +13,7 @@ import numpy as np
sys.path[0] = os.path.join(os.path.dirname("__file__"), os.path.pardir)
import models
from utility import add_arguments, print_arguments, _download, _decompress
from paddleslim.dist import merge, l2, soft_label, fsp
from paddleslim.dist import merge, l2, soft_label
logging.basicConfig(format='%(asctime)s-%(levelname)s: %(message)s')
_logger = logging.getLogger(__name__)
......@@ -99,11 +99,11 @@ def compress(args):
) if args.use_gpu else paddle.static.cpu_places()
place = places[0]
if args.use_gpu:
devices_num = paddle.fluid.core.get_cuda_device_count()
devices_num = paddle.framework.core.get_cuda_device_count()
else:
devices_num = int(os.environ.get('CPU_NUM', 1))
with paddle.static.program_guard(student_program, s_startup):
with paddle.fluid.unique_name.guard():
with paddle.utils.unique_name.guard():
image = paddle.static.data(
name='image', shape=[None] + image_shape, dtype='float32')
label = paddle.static.data(
......@@ -144,7 +144,7 @@ def compress(args):
teacher_program = paddle.static.Program()
t_startup = paddle.static.Program()
with paddle.static.program_guard(teacher_program, t_startup):
with paddle.fluid.unique_name.guard():
with paddle.utils.unique_name.guard():
image = paddle.static.data(
name='image', shape=[None] + image_shape, dtype='float32')
predict = teacher_model.net(image, class_dim=class_dim)
......
demo/mkldnn_quant/sample_tester.py
浏览文件 @ 044909e7
......@@ -24,7 +24,7 @@ import time
import paddle
import paddle.fluid as fluid
from paddle.fluid.framework import IrGraph
from paddle.fluid import core
from paddle.framework import core
logging.basicConfig(format='%(asctime)s-%(levelname)s: %(message)s')
_logger = logging.getLogger(__name__)
......@@ -244,7 +244,7 @@ class SampleTester(unittest.TestCase):
return outputs, acc1_avg, acc5_avg, fps_avg, latency_avg
def test_graph_transformation(self):
if not fluid.core.is_compiled_with_mkldnn():
if not paddle.fluid.core.is_compiled_with_mkldnn():
return
infer_model_path = test_case_args.infer_model
......
demo/models/mobilenet.py
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......@@ -2,7 +2,6 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
import paddle.fluid as fluid
from paddle.nn.initializer import KaimingUniform
__all__ = ['MobileNet']
......@@ -121,13 +120,8 @@ class MobileNet():
scale=scale,
name="conv6")
input = fluid.layers.pool2d(
input=input,
pool_size=0,
pool_stride=1,
pool_type='avg',
global_pooling=True)
with fluid.name_scope('last_fc'):
input = paddle.nn.functional.adaptive_avg_pool2d(input, 1)
with paddle.static.name_scope('last_fc'):
output = paddle.static.nn.fc(
input,
class_dim,
......
demo/models/mobilenet_v2.py
浏览文件 @ 044909e7
......@@ -16,7 +16,6 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
import paddle.fluid as fluid
from paddle.nn.initializer import KaimingUniform
__all__ = [
......@@ -101,12 +100,7 @@ class MobileNetV2():
if_act=True,
name='conv9')
input = fluid.layers.pool2d(
input=input,
pool_size=7,
pool_stride=1,
pool_type='avg',
global_pooling=True)
input = paddle.nn.functional.adaptive_avg_pool2d(input, 1)
output = paddle.static.nn.fc(
input,
......@@ -150,7 +144,7 @@ class MobileNetV2():
return bn
def shortcut(self, input, data_residual):
return fluid.layers.elementwise_add(input, data_residual)
return paddle.add(input, data_residual)
def inverted_residual_unit(self,
input,
......
demo/models/mobilenet_v3.py
浏览文件 @ 044909e7
import paddle
import paddle.fluid as fluid
from paddle.nn.initializer import KaimingUniform
import math
......@@ -103,8 +102,7 @@ class MobileNetV3():
if_act=True,
act='hard_swish',
name='conv_last')
conv = fluid.layers.pool2d(
input=conv, pool_type='avg', global_pooling=True, use_cudnn=False)
conv = paddle.nn.functional.adaptive_avg_pool2d(conv, 1)
conv = paddle.static.nn.conv2d(
input=conv,
num_filters=cls_ch_expand,
......@@ -114,7 +112,7 @@ class MobileNetV3():
act=None,
param_attr=paddle.ParamAttr(name='last_1x1_conv_weights'),
bias_attr=False)
conv = fluid.layers.hard_swish(conv)
conv = paddle.nn.functional.hardswish(conv)
out = paddle.static.nn.fc(
conv,
class_dim,
......@@ -149,19 +147,17 @@ class MobileNetV3():
input=conv,
param_attr=paddle.ParamAttr(
name=bn_name + "_scale",
regularizer=fluid.regularizer.L2DecayRegularizer(
regularization_coeff=0.0)),
regularizer=paddle.regularizer.L2Decay(coeff=0.0)),
bias_attr=paddle.ParamAttr(
name=bn_name + "_offset",
regularizer=fluid.regularizer.L2DecayRegularizer(
regularization_coeff=0.0)),
regularizer=paddle.regularizer.L2Decay(coeff=0.0)),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance')
if if_act:
if act == 'relu':
bn = paddle.nn.functional.relu(bn)
elif act == 'hard_swish':
bn = fluid.layers.hard_swish(bn)
bn = paddle.nn.functional.hardswish(bn)
return bn
def hard_swish(self, x):
......@@ -169,8 +165,7 @@ class MobileNetV3():
def se_block(self, input, num_out_filter, ratio=4, name=None):
num_mid_filter = int(num_out_filter // ratio)
pool = fluid.layers.pool2d(
input=input, pool_type='avg', global_pooling=True, use_cudnn=False)
pool = paddle.nn.functional.adaptive_avg_pool2d(input, 1)
conv1 = paddle.static.nn.conv2d(
input=pool,
filter_size=1,
......@@ -186,7 +181,7 @@ class MobileNetV3():
param_attr=paddle.ParamAttr(name=name + '_2_weights'),
bias_attr=paddle.ParamAttr(name=name + '_2_offset'))
scale = fluid.layers.elementwise_mul(x=input, y=conv2, axis=0)
scale = paddle.multiply(x=input, y=conv2)
return scale
def residual_unit(self,
......@@ -224,7 +219,7 @@ class MobileNetV3():
name=name + '_depthwise')
if use_se:
with fluid.name_scope('se_block_skip'):
with paddle.static.name_scope('se_block_skip'):
conv1 = self.se_block(
input=conv1,
num_out_filter=num_mid_filter,
......@@ -241,7 +236,7 @@ class MobileNetV3():
if num_in_filter != num_out_filter or stride != 1:
return conv2
else:
return fluid.layers.elementwise_add(x=input_data, y=conv2, act=None)
return paddle.add(x=input_data, y=conv2)
def MobileNetV3_small_x0_25():
......
demo/models/pvanet.py
浏览文件 @ 044909e7
......@@ -227,10 +227,8 @@ class PVANet():
is_bias=True,
default_initializer=paddle.nn.initializer.Constant(value=0.0))
output = fluid.layers.elementwise_mul(
input, scale_param, axis=axis, name=prefix + 'mul')
output = fluid.layers.elementwise_add(
output, offset_param, axis=axis, name=prefix + 'add')
output = paddle.multiply(input, scale_param, name=prefix + 'mul')
output = paddle.add(output, offset_param, name=prefix + 'add')
return output
def _conv(self,
......@@ -293,7 +291,7 @@ class PVANet():
def _bn_crelu(self, input, name):
net = self._bn(input, None, name + '_bn_1')
neg_net = fluid.layers.scale(net, scale=-1.0, name=name + '_neg')
neg_net = paddle.scale(net, scale=-1.0, name=name + '_neg')
net = paddle.concat([net, neg_net], axis=1)
net = self._scale(net, name + '_scale')
net = paddle.nn.functional.relu(net, name=name + '_relu')
......@@ -369,13 +367,13 @@ def Fpn_Fusion(blocks, net):
if i == 0:
g[i] = net.deconv_bn_layer(h[i], num_outputs[i], name='fpn_0')
else:
out = fluid.layers.elementwise_add(x=g[i - 1], y=h[i])
out = paddle.add(x=g[i - 1], y=h[i])
out = net.conv_bn_layer(out, num_outputs[i], 1,
'fpn_trans_' + str(i))
g[i] = net.deconv_bn_layer(
out, num_outputs[i], name='fpn_' + str(i))
out = fluid.layers.elementwise_add(x=g[-2], y=h[-1])
out = paddle.add(x=g[-2], y=h[-1])
out = net.conv_bn_layer(out, num_outputs[-1], 1, 'fpn_post_0')
out = net.conv_bn_layer(out, num_outputs[-1], 3, 'fpn_post_1')
......@@ -409,7 +407,7 @@ def Detector_Header(f_common, net, class_num):
bias_attr=paddle.ParamAttr(name=name + '_conv_bias'),
name=name + '_conv')
f_score = fluid.layers.transpose(f_score, perm=[0, 2, 3, 1])
f_score = paddle.transpose(f_score, perm=[0, 2, 3, 1])
f_score = paddle.reshape(f_score, shape=[-1, class_num + 1])
f_score = paddle.nn.functional.softmax(input=f_score)
......@@ -448,7 +446,7 @@ def inference(input, class_num=1, nms_thresh=0.2, score_thresh=0.5):
f_score, f_geo = east(input, class_num)
print("f_geo shape={}".format(f_geo.shape))
print("f_score shape={}".format(f_score.shape))
f_score = fluid.layers.transpose(f_score, perm=[1, 0])
f_score = paddle.transpose(f_score, perm=[1, 0])
return f_score, f_geo
......@@ -460,20 +458,20 @@ def loss(f_score, f_geo, l_score, l_geo, l_mask, class_num=1):
'''
#smooth_l1_loss
channels = 8
l_geo_split, l_short_edge = fluid.layers.split(
l_geo_split, l_short_edge = paddle.split(
l_geo, num_or_sections=[channels, 1],
dim=1) #last channel is short_edge_norm
f_geo_split = fluid.layers.split(f_geo, num_or_sections=[channels], dim=1)
f_geo_split = paddle.split(f_geo, num_or_sections=[channels], dim=1)
f_geo_split = f_geo_split[0]
geo_diff = l_geo_split - f_geo_split
abs_geo_diff = fluid.layers.abs(geo_diff)
abs_geo_diff = paddle.abs(geo_diff)
l_flag = l_score >= 1
l_flag = fluid.layers.cast(x=l_flag, dtype="float32")
l_flag = paddle.cast(x=l_flag, dtype="float32")
l_flag = fluid.layers.expand(x=l_flag, expand_times=[1, channels, 1, 1])
smooth_l1_sign = abs_geo_diff < l_flag
smooth_l1_sign = fluid.layers.cast(x=smooth_l1_sign, dtype="float32")
smooth_l1_sign = paddle.cast(x=smooth_l1_sign, dtype="float32")
in_loss = abs_geo_diff * abs_geo_diff * smooth_l1_sign + (
abs_geo_diff - 0.5) * (1.0 - smooth_l1_sign)
......@@ -481,18 +479,19 @@ def loss(f_score, f_geo, l_score, l_geo, l_mask, class_num=1):
x=l_short_edge, expand_times=[1, channels, 1, 1])
out_loss = l_short_edge * in_loss * l_flag
out_loss = out_loss * l_flag
smooth_l1_loss = fluid.layers.reduce_mean(out_loss)
smooth_l1_loss = paddle.mean(out_loss)
##softmax_loss
l_score.stop_gradient = True
l_score = fluid.layers.transpose(l_score, perm=[0, 2, 3, 1])
l_score = paddle.transpose(l_score, perm=[0, 2, 3, 1])
l_score.stop_gradient = True
l_score = paddle.reshape(l_score, shape=[-1, 1])
l_score.stop_gradient = True
l_score = fluid.layers.cast(x=l_score, dtype="int64")
l_score = paddle.cast(x=l_score, dtype="int64")
l_score.stop_gradient = True
softmax_loss = fluid.layers.cross_entropy(input=f_score, label=l_score)
softmax_loss = fluid.layers.reduce_mean(softmax_loss)
softmax_loss = paddle.nn.functional.cross_entropy(
input=f_score, label=l_score)
softmax_loss = paddle.mean(softmax_loss)
return softmax_loss, smooth_l1_loss
demo/models/resnet.py
浏览文件 @ 044909e7
......@@ -184,8 +184,8 @@ class ResNet():
is_first=False,
name=name + "_branch1")
return fluid.layers.elementwise_add(
x=short, y=conv2, act='relu', name=name + ".add.output.5")
out = paddle.add(x=short, y=conv2, name=name + ".add.output.5")
return paddle.nn.functional.relu(out)
def basic_block(self, input, num_filters, stride, is_first, name):
conv0 = self.conv_bn_layer(
......@@ -203,7 +203,9 @@ class ResNet():
name=name + "_branch2b")
short = self.shortcut(
input, num_filters, stride, is_first, name=name + "_branch1")
return fluid.layers.elementwise_add(x=short, y=conv1, act='relu')
out = paddle.add(x=short, y=conv1)
return paddle.nn.functional.relu(out)
def ResNet34(prefix_name=''):
......
demo/models/resnet_vd.py
浏览文件 @ 044909e7
......@@ -234,8 +234,8 @@ class ResNet():
stride,
if_first=if_first,
name=name + "_branch1")
return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
out = paddle.add(x=short, y=conv2)
return paddle.nn.functional.relu(out)
def basic_block(self, input, num_filters, stride, name, if_first):
conv0 = self.conv_bn_layer(
......@@ -257,7 +257,7 @@ class ResNet():
stride,
if_first=if_first,
name=name + "_branch1")
return fluid.layers.elementwise_add(x=short, y=conv1, act='relu')
return paddle.add(x=short, y=conv1, act='relu')
def ResNet18_vd():
......
demo/models/slimfacenet.py
浏览文件 @ 044909e7
......@@ -172,8 +172,8 @@ class SlimFaceNet():
out = self.arc_margin_product(
x, label, self.class_dim, s=32.0, m=0.50, mode=2)
softmax = paddle.nn.functional.softmax(input=out)
cost = fluid.layers.cross_entropy(input=softmax, label=label)
loss = fluid.layers.mean(x=cost)
cost = paddle.nn.functional.cross_entropy(input=softmax, label=label)
loss = paddle.mean(x=cost)
acc = paddle.static.accuracy(input=out, label=label, k=1)
return loss, acc
......@@ -227,8 +227,9 @@ class SlimFaceNet():
if num_in_filter != num_out_filter or stride != 1:
return linear_conv
else:
return fluid.layers.elementwise_add(
x=input_data, y=linear_conv, act=None)
out = paddle.add(x=input_data, y=linear_conv, act=None)
return paddle.nn.functional.relu(out)
def se_block(self, input, num_out_filter, ratio=4, name=None):
num_mid_filter = int(num_out_filter // ratio)
......@@ -241,7 +242,7 @@ class SlimFaceNet():
act=None,
param_attr=paddle.ParamAttr(name=name + '_1_weights'),
bias_attr=paddle.ParamAttr(name=name + '_1_offset'))
conv1 = fluid.layers.prelu(
conv1 = paddle.static.nn.prelu(
conv1,
mode='channel',
param_attr=paddle.ParamAttr(
......@@ -254,7 +255,7 @@ class SlimFaceNet():
act='hard_sigmoid',
param_attr=paddle.ParamAttr(name=name + '_2_weights'),
bias_attr=paddle.ParamAttr(name=name + '_2_offset'))
scale = fluid.layers.elementwise_mul(x=input, y=conv2, axis=0)
scale = paddle.multiply(x=input, y=conv2)
return scale
def conv_bn_layer(self,
......@@ -287,7 +288,7 @@ class SlimFaceNet():
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance')
if if_act:
return fluid.layers.prelu(
return paddle.static.nn.prelu(
bn,
mode='channel',
param_attr=paddle.ParamAttr(
......@@ -297,10 +298,8 @@ class SlimFaceNet():
return bn
def arc_margin_product(self, input, label, out_dim, s=32.0, m=0.50, mode=2):
input_norm = fluid.layers.sqrt(
fluid.layers.reduce_sum(
paddle.square(input), dim=1))
input = fluid.layers.elementwise_div(input, input_norm, axis=0)
input_norm = paddle.sqrt(paddle.sum(paddle.square(input), dim=1))
input = paddle.divide(input, input_norm, axis=0)
weight = paddle.static.create_parameter(
shape=[out_dim, input.shape[1]],
......@@ -310,13 +309,11 @@ class SlimFaceNet():
initializer=paddle.nn.initializer.Xavier(),
regularizer=fluid.regularizer.L2Decay(4e-4)))
weight_norm = fluid.layers.sqrt(
fluid.layers.reduce_sum(
paddle.square(weight), dim=1))
weight = fluid.layers.elementwise_div(weight, weight_norm, axis=0)
weight = fluid.layers.transpose(weight, perm=[1, 0])
weight_norm = paddle.sqrt(paddle.sum(paddle.square(weight), dim=1))
weight = paddle.divide(weight, weight_norm, axis=0)
weight = paddle.transpose(weight, perm=[1, 0])
cosine = fluid.layers.mul(input, weight)
sine = fluid.layers.sqrt(1.0 - paddle.square(cosine))
sine = paddle.sqrt(1.0 - paddle.square(cosine))
cos_m = math.cos(m)
sin_m = math.sin(m)
......@@ -333,16 +330,14 @@ class SlimFaceNet():
pass
one_hot = fluid.layers.one_hot(input=label, depth=out_dim)
output = fluid.layers.elementwise_mul(
one_hot, phi) + fluid.layers.elementwise_mul(
(1.0 - one_hot), cosine)
output = paddle.multiply(one_hot, phi) + paddle.multiply(
(1.0 - one_hot), cosine)
output = output * s
return output
def paddle_where_more_than(self, target, limit, x, y):
mask = fluid.layers.cast(x=(target > limit), dtype='float32')
output = fluid.layers.elementwise_mul(
mask, x) + fluid.layers.elementwise_mul((1.0 - mask), y)
mask = paddle.cast(x=(target > limit), dtype='float32')
output = paddle.multiply(mask, x) + paddle.multiply((1.0 - mask), y)
return output
......
demo/ofa/ernie/ernie_supernet/modeling_ernie_supernet.py
浏览文件 @ 044909e7
......@@ -31,6 +31,7 @@ if six.PY2:
else:
from pathlib import Path
import paddle
import paddle.fluid.dygraph as D
import paddle.fluid as F
import paddle.fluid.layers as L
......@@ -73,14 +74,14 @@ def _attn_forward(self,
else:
n_head = self.n_head
q = L.transpose(
L.reshape(q, [0, 0, n_head, q.shape[-1] // n_head]),
q = paddle.transpose(
paddle.reshape(q, [0, 0, n_head, q.shape[-1] // n_head]),
[0, 2, 1, 3]) #[batch, head, seq, dim]
k = L.transpose(
L.reshape(k, [0, 0, n_head, k.shape[-1] // n_head]),
k = paddle.transpose(
paddle.reshape(k, [0, 0, n_head, k.shape[-1] // n_head]),
[0, 2, 1, 3]) #[batch, head, seq, dim]
v = L.transpose(
L.reshape(v, [0, 0, n_head, v.shape[-1] // n_head]),
v = paddle.transpose(
paddle.reshape(v, [0, 0, n_head, v.shape[-1] // n_head]),
[0, 2, 1, 3]) #[batch, head, seq, dim]
q = L.scale(q, scale=self.d_key**-0.5)
......@@ -275,7 +276,7 @@ def _seqence_forward(self, *args, **kwargs):
if len(labels.shape) == 1:
labels = L.reshape(labels, [-1, 1])
loss = L.softmax_with_cross_entropy(logits, labels)
loss = L.reduce_mean(loss)
loss = paddle.mean(loss)
else:
loss = None
return loss, logits, additional_info
......
demo/ofa/ernie/ofa_ernie.py
浏览文件 @ 044909e7
......@@ -46,7 +46,7 @@ from paddleslim.nas.ofa.convert_super import Convert, supernet
def soft_cross_entropy(inp, target):
inp_likelihood = L.log_softmax(inp, axis=-1)
target_prob = L.softmax(target, axis=-1)
return -1. * L.mean(L.reduce_sum(inp_likelihood * target_prob, dim=-1))
return -1. * L.mean(paddle.sum(inp_likelihood * target_prob, dim=-1))
if __name__ == '__main__':
......
demo/optimizer.py
浏览文件 @ 044909e7
......@@ -62,7 +62,7 @@ class Optimizer(object):
self.decay_rate = decay_rate
self.total_images = total_images
if args.use_gpu:
devices_num = paddle.fluid.core.get_cuda_device_count()
devices_num = paddle.framework.core.get_cuda_device_count()
else:
devices_num = int(os.environ.get('CPU_NUM', 1))
......
demo/quant/quant_embedding/cluster_train.py
浏览文件 @ 044909e7
......@@ -182,7 +182,7 @@ def train_loop(args, train_program, reader, py_reader, loss, trainer_id, weight,
print("model saved in %s" % model_dir)
batch_id += 1
except paddle.fluid.core.EOFException:
except paddle.framework.core.EOFException:
py_reader.reset()
epoch_end = time.time()
logger.info("Epoch: {0}, Train total expend: {1} ".format(
......
demo/quant/quant_embedding/net.py
浏览文件 @ 044909e7
......@@ -86,7 +86,7 @@ def skip_gram_word2vec(dict_size, embedding_size, is_sparse=False, neg_num=5):
neg_emb_b_vec = paddle.reshape(neg_emb_b, shape=[-1, neg_num])
true_logits = paddle.add(paddle.mean(
paddle.multiply(input_emb, true_emb_w), axis=1, keepdim=True),
paddle.multiply(input_emb, true_emb_w), keepdim=True),
true_emb_b)
input_emb_re = paddle.reshape(input_emb, shape=[-1, 1, embedding_size])
neg_matmul = fluid.layers.matmul(
......
demo/quant/quant_embedding/train.py
浏览文件 @ 044909e7
......@@ -176,7 +176,7 @@ def train_loop(args, train_program, reader, py_reader, loss, trainer_id, weight,
print("model saved in %s" % model_dir)
batch_id += 1
except paddle.fluid.core.EOFException:
except paddle.framework.core.EOFException:
py_reader.reset()
epoch_end = time.time()
logger.info("Epoch: {0}, Train total expend: {1} ".format(
......
demo/slimfacenet/train_eval.py
浏览文件 @ 044909e7
......@@ -151,17 +151,17 @@ def train(exe, train_program, train_out, test_program, test_out, args):
def build_program(program, startup, args, is_train=True):
if args.use_gpu:
num_trainers = fluid.core.get_cuda_device_count()
num_trainers = paddle.device.cuda.device_count()
else:
num_trainers = int(os.environ.get('CPU_NUM', 1))
places = fluid.cuda_places() if args.use_gpu else paddle.CPUPlace()
places = paddle.static.cuda_places() if args.use_gpu else paddle.CPUPlace()
train_dataset = CASIA_Face(root=args.train_data_dir)
trainset_scale = len(train_dataset)
with paddle.static.program_guard(
main_program=program, startup_program=startup):
with fluid.unique_name.guard():
with paddle.utils.unique_name.guard():
# Model construction
model = models.__dict__[args.model](
class_dim=train_dataset.class_nums)
......@@ -215,7 +215,7 @@ def build_program(program, startup, args, is_train=True):
return_list=False)
reader.set_sample_list_generator(
test_reader,
places=fluid.cuda_places()
places=paddle.static.cuda_places()
if args.use_gpu else paddle.CPUPlace())
model.extract_feature = True
......@@ -296,7 +296,7 @@ def main():
args = parser.parse_args()
if args.use_gpu:
num_trainers = fluid.core.get_cuda_device_count()
num_trainers = paddle.fluid.core.get_cuda_device_count()
else:
num_trainers = int(os.environ.get('CPU_NUM', 1))
print(args)
......@@ -366,7 +366,8 @@ def main():
return_list=False)
reader.set_sample_list_generator(
test_reader,
places=fluid.cuda_places() if args.use_gpu else paddle.CPUPlace())
places=paddle.static.cuda_places()
if args.use_gpu else paddle.CPUPlace())
test_out = (fetch_targets, reader, flods, flags)
print('fetch_targets[0]: ', fetch_targets[0])
print('feed_target_names: ', feed_target_names)
......
paddleslim/common/recover_program.py
浏览文件 @ 044909e7
......@@ -45,7 +45,7 @@ def _recover_reserve_space_with_bn(program):
generate_with_ignorable_key(".".join(
["reserve_space", 'tmp'])),
dtype=block.var(op.input("X")[0]).dtype,
type=paddle.fluid.core.VarDesc.VarType.LOD_TENSOR,
type=paddle.framework.core.VarDesc.VarType.LOD_TENSOR,
persistable=False,
stop_gradient=True)
op.desc.set_output("ReserveSpace", [reserve_space.name])
......
paddleslim/common/rl_controller/lstm/lstm_controller.py
浏览文件 @ 044909e7
......@@ -113,7 +113,7 @@ class LSTM(RLBaseController):
entropies = []
sample_log_probs = []
with fluid.unique_name.guard('Controller'):
with paddle.utils.unique_name.guard('Controller'):
self._create_parameter()
inputs = self.g_emb
......
paddleslim/dygraph/prune/pruning_plan.py
浏览文件 @ 044909e7
......@@ -114,7 +114,7 @@ class PruningPlan():
elif p.is_cuda_pinned_place():
place = paddle.CUDAPinnedPlace()
else:
p = paddle.fluid.core.Place()
p = paddle.framework.core.Place()
p.set_place(t_value._place())
place = paddle.CUDAPlace(p.gpu_device_id())
......@@ -153,7 +153,7 @@ class PruningPlan():
elif p.is_cuda_pinned_place():
place = paddle.CUDAPinnedPlace()
else:
p = paddle.fluid.core.Place()
p = paddle.framework.core.Place()
p.set_place(t_value._place())
place = paddle.CUDAPlace(p.gpu_device_id())
......@@ -190,12 +190,15 @@ class PruningPlan():
"float32")
p = t_value._place()
print(f"hit pruning plan--------------------")
if p.is_cpu_place():
print(f"hit pruning plan cpu--------------------")
place = paddle.CPUPlace()
elif p.is_cuda_pinned_place():
place = paddle.CUDAPinnedPlace()
else:
p = paddle.fluid.core.Place()
print(f"hit pruning plan gpu--------------------")
p = paddle.framework.core.Place()
p.set_place(t_value._place())
place = paddle.CUDAPlace(p.gpu_device_id())
......@@ -246,7 +249,7 @@ class PruningPlan():
elif p.is_cuda_pinned_place():
place = paddle.CUDAPinnedPlace()
else:
p = paddle.fluid.core.Place()
p = paddle.framework.core.Place()
p.set_place(t_value._place())
place = paddle.CUDAPlace(p.gpu_device_id())
t_value.set(pruned_value, place)
......@@ -274,7 +277,7 @@ class PruningPlan():
elif p.is_cuda_pinned_place():
place = paddle.CUDAPinnedPlace()
else:
p = paddle.fluid.core.Place()
p = paddle.framework.core.Place()
p.set_place(t_value._place())
place = paddle.CUDAPlace(p.gpu_device_id())
......
paddleslim/nas/darts/architect.py
浏览文件 @ 044909e7
......@@ -102,8 +102,7 @@ class Architect(object):
def _hessian_vector_product(self, vector, input, target, r=1e-2):
R = r * paddle.rsqrt(
paddle.fluid.layers.sum(
[paddle.sum(x=paddle.square(v)) for v in vector]))
paddle.add_n([paddle.sum(x=paddle.square(v)) for v in vector]))
model_params = [
p for p in self.model.parameters()
......
paddleslim/nas/ofa/ofa.py
浏览文件 @ 044909e7
......@@ -610,7 +610,8 @@ class OFA(OFABase):
print(f"hit cpu in ofa-------------------------------")
place = paddle.CPUPlace()
else:
place = paddle.fluid.core.CUDAPlace(p.gpu_device_id())
place = paddle.framework.core.CUDAPlace(p.gpu_device_id(
))
t_value.set(pruned_state_dict[name], place)
if super_model_state_dict != None and len(super_model_state_dict) != 0:
......
paddleslim/nas/ofa/utils/utils.py
浏览文件 @ 044909e7
......@@ -60,20 +60,6 @@ def set_state_dict(model, state_dict):
_logger.info('{} is not in state_dict'.format(tmp_n))
def to_tensor(string_values, name="text"):
"""
Create the tensor that the value holds the list of string.
NOTICE: The value will be holded in the cpu place.
Parameters:
string_values(list[string]): The value will be setted to the tensor.
name(string): The name of the tensor.
"""
tensor = paddle.Tensor(paddle.fluid.core.VarDesc.VarType.STRING, [], name,
paddle.fluid.core.VarDesc.VarType.STRINGS, False)
tensor.value().set_string_list(string_values)
return tensor
def build_input(input_size, dtypes):
if isinstance(input_size, list) and all(
isinstance(i, numbers.Number) for i in input_size):
......@@ -81,8 +67,10 @@ def build_input(input_size, dtypes):
dtype = dtypes[0]
else:
dtype = dtypes
if dtype == paddle.fluid.core.VarDesc.VarType.STRINGS:
return to_tensor([""])
# if dtype == paddle.framework.core.VarDesc.VarType.STRINGS:
# return to_tensor([""])
# TODO: Uncommet Add unittest for strings dtype
assert dtype != paddle.framework.core.VarDesc.VarType.STRINGS
return paddle.cast(paddle.rand(list(input_size)), dtype)
if isinstance(input_size, dict):
inputs = {}
......
paddleslim/quant/analysis_qat.py
浏览文件 @ 044909e7
......@@ -20,7 +20,7 @@ import logging
import numpy as np
import paddle
from paddle.fluid import core
from paddle.framework import core
from paddle.fluid.framework import IrGraph
from ..common import get_logger, load_inference_model
......
paddleslim/quant/quant_aware_with_infermodel.py
浏览文件 @ 044909e7
......@@ -26,7 +26,7 @@ import paddle
from ..common.recover_program import recover_inference_program
from .quanter import _quant_config_default, _parse_configs, pact, get_pact_optimizer
from .quanter import quant_aware, convert
from ..dist import merge, l2, soft_label, fsp
from ..dist import merge, l2, soft_label
from ..auto_compression.create_compressed_program import build_distill_program
import logging
logging.getLogger().setLevel(logging.INFO)
......
paddleslim/quant/quant_embedding.py
浏览文件 @ 044909e7
......@@ -153,8 +153,8 @@ def _clear_var(var_name, scope):
def _get_var_dtype(config):
return paddle.fluid.core.VarDesc.VarType.INT8 if config['dtype'] == 'int8' \
else paddle.fluid.core.VarDesc.VarType.INT16
return paddle.framework.core.VarDesc.VarType.INT8 if config['dtype'] == 'int8' \
else paddle.framework.core.VarDesc.VarType.INT16
def _quant_embedding_abs_max(graph, scope, place, config, var_name,
......@@ -189,7 +189,7 @@ def _quant_embedding_abs_max(graph, scope, place, config, var_name,
name=_get_dequant_var_name(var_node.name()),
var_type=var_node.type(),
shape=var_node.shape(),
var_dtype=paddle.fluid.core.VarDesc.VarType.FP32)
var_dtype=paddle.framework.core.VarDesc.VarType.FP32)
scope.var(dequant_var_node.name())
max_range = (1 << (config['quantize_bits'] - 1)) - 1
......@@ -199,7 +199,7 @@ def _quant_embedding_abs_max(graph, scope, place, config, var_name,
attrs={
'max_range': float(max_range),
'op_role':
paddle.fluid.core.op_proto_and_checker_maker.OpRole.Forward
paddle.framework.core.op_proto_and_checker_maker.OpRole.Forward
},
inputs={'X': var_node,
'Scale': scale_node},
......@@ -232,7 +232,7 @@ def _quant_embedding_abs_max(graph, scope, place, config, var_name,
_get_scale_var_name(var_name),
var_type=embedding_node.type(),
shape=[1],
var_dtype=paddle.fluid.core.VarDesc.VarType.FP32)
var_dtype=paddle.framework.core.VarDesc.VarType.FP32)
quant_tensor_var = graph.create_persistable_node(
_get_quant_var_name(var_name),
var_type=embedding_node.type(),
......@@ -318,7 +318,7 @@ def _quant_embedding_log(graph, scope, place, config, var_name, embedding_node):
name=_get_dequant_var_name(var_node.name()),
var_type=var_node.type(),
shape=var_node.shape(),
var_dtype=paddle.fluid.core.VarDesc.VarType.FP32)
var_dtype=paddle.framework.core.VarDesc.VarType.FP32)
scope.var(dequant_var_node.name())
output_ops = var_node.outputs
......@@ -326,7 +326,7 @@ def _quant_embedding_log(graph, scope, place, config, var_name, embedding_node):
op_type='dequantize_log',
attrs={
'op_role':
paddle.fluid.core.op_proto_and_checker_maker.OpRole.Forward
paddle.framework.core.op_proto_and_checker_maker.OpRole.Forward
},
inputs={'X': var_node,
'Dict': topk_num_node},
......@@ -350,12 +350,12 @@ def _quant_embedding_log(graph, scope, place, config, var_name, embedding_node):
_get_dict_var_name(var_name),
var_type=embedding_node.type(),
shape=topk_num.shape,
var_dtype=paddle.fluid.core.VarDesc.VarType.FP32)
var_dtype=paddle.framework.core.VarDesc.VarType.FP32)
quant_tensor_var = graph.create_persistable_node(
_get_quant_var_name(var_name),
var_type=embedding_node.type(),
shape=embedding_node.shape(),
var_dtype=paddle.fluid.core.VarDesc.VarType.INT8)
var_dtype=paddle.framework.core.VarDesc.VarType.INT8)
# create var in scope
scope.var(_get_quant_var_name(var_name))
scope.var(_get_dict_var_name(var_name))
......@@ -393,7 +393,7 @@ def _split_embedding_seq_pool(graph, op):
out = outputs[0]
lookup_out = graph.create_var_node(
name=ids.name() + '.look_up_table.out',
var_type=paddle.fluid.core.VarDesc.VarType.LOD_TENSOR,
var_type=paddle.framework.core.VarDesc.VarType.LOD_TENSOR,
shape=[1],
var_dtype=weight.dtype())
lookup_table_op = graph.create_op_node(
......@@ -411,7 +411,7 @@ def _split_embedding_seq_pool(graph, op):
graph.link_to(lookup_table_op, lookup_out)
max_index = graph.create_var_node(
name=ids.name() + '.seq_pool_op.max_index',
var_type=paddle.fluid.core.VarDesc.VarType.LOD_TENSOR,
var_type=paddle.framework.core.VarDesc.VarType.LOD_TENSOR,
shape=[1],
var_dtype=weight.dtype())
......@@ -452,7 +452,7 @@ def quant_embedding(program, place, config=None, scope=None):
scope = paddle.static.global_scope() if scope is None else scope
graph = paddle.fluid.framework.IrGraph(
paddle.fluid.core.Graph(program.desc), for_test=True)
paddle.framework.core.Graph(program.desc), for_test=True)
quantize_params_map = {}
all_op = graph.all_op_nodes()
for op in all_op:
......
paddleslim/quant/quanter.py
浏览文件 @ 044909e7
......@@ -20,7 +20,7 @@ import collections
import numpy as np
import paddle
from paddle.fluid import core
from paddle.framework import core
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.framework import IrGraph
from paddle.fluid.contrib.slim.quantization import WeightQuantization
......@@ -799,14 +799,10 @@ def pact(x, name=None):
regularizer=paddle.fluid.regularizer.L2Decay(0.0001),
learning_rate=1)
u_param = helper.create_parameter(attr=u_param_attr, shape=[1], dtype=dtype)
x = paddle.fluid.layers.elementwise_sub(
x,
paddle.nn.functional.relu(
paddle.fluid.layers.elementwise_sub(x, u_param)))
x = paddle.fluid.layers.elementwise_add(
x,
paddle.nn.functional.relu(
paddle.fluid.layers.elementwise_sub(-u_param, x)))
x = paddle.subtract(x,
paddle.nn.functional.relu(paddle.subtract(x, u_param)))
x = paddle.paddle.add(
x, paddle.nn.functional.relu(paddle.subtract(-u_param, x)))
return x
......
tests/dygraph/test_prune.py
浏览文件 @ 044909e7
......@@ -28,6 +28,7 @@ class TestPrune(unittest.TestCase):
def runTest(self):
static_shapes = self.static_prune(self._net, self._ratios)
dygraph_shapes = self.dygraph_prune(self._net, self._ratios)
lazy_dygraph_shapes = self.dygraph_lazy_prune(self._net, self._ratios)
all_right = True
for _name, _shape in static_shapes.items():
if dygraph_shapes[_name] != list(_shape):
......@@ -37,11 +38,18 @@ class TestPrune(unittest.TestCase):
all_right = False
self.assertTrue(all_right)
def dygraph_prune(self, net, ratios):
def dygraph_lazy_prune(self, net, ratios):
if paddle.framework.core.is_compiled_with_cuda():
paddle.set_device('gpu')
self.dygraph_prune(net, ratios, apply="lazy")
paddle.set_device('cpu')
return self.dygraph_prune(net, ratios, apply="lazy")
def dygraph_prune(self, net, ratios, apply="impretive"):
paddle.disable_static()
model = net(pretrained=False)
pruner = L1NormFilterPruner(model, [1, 3, 16, 16])
pruner.prune_vars(ratios, 0)
pruner.prune_vars(ratios, 0, apply=apply)
shapes = {}
for param in model.parameters():
shapes[param.name] = param.shape
......@@ -52,7 +60,7 @@ class TestPrune(unittest.TestCase):
paddle.enable_static()
main_program = paddle.static.Program()
startup_program = paddle.static.Program()
with paddle.fluid.unique_name.guard():
with paddle.utils.unique_name.guard():
with paddle.static.program_guard(main_program, startup_program):
input = paddle.static.data(
name="image", shape=[None, 3, 16, 16])
......@@ -61,7 +69,7 @@ class TestPrune(unittest.TestCase):
place = paddle.CPUPlace()
exe = paddle.static.Executor(place)
scope = paddle.fluid.Scope()
scope = paddle.static.Scope()
exe.run(startup_program, scope=scope)
pruner = Pruner()
main_program, _, _ = pruner.prune(
......
tests/dygraph/test_sensitivity.py
浏览文件 @ 044909e7
......@@ -98,7 +98,7 @@ class TestSensitivity(unittest.TestCase):
paddle.enable_static()
main_program = paddle.static.Program()
startup_program = paddle.static.Program()
with paddle.fluid.unique_name.guard():
with paddle.utils.unique_name.guard():
with paddle.static.program_guard(main_program, startup_program):
input = paddle.static.data(
name="image", shape=[None, 1, 28, 28])
......
tests/test_autoprune.py
浏览文件 @ 044909e7
......@@ -53,7 +53,7 @@ class TestPrune(StaticCase):
val_program = paddle.static.default_main_program().clone(for_test=True)
place = paddle.CPUPlace()
exe = paddle.static.Executor(place)
scope = paddle.fluid.Scope()
scope = paddle.static.Scope()
exe.run(startup_program, scope=scope)
pruner = AutoPruner(
......
tests/test_fpgm_prune.py
浏览文件 @ 044909e7
......@@ -48,7 +48,7 @@ class TestPrune(StaticCase):
place = paddle.CPUPlace()
exe = paddle.static.Executor(place)
scope = paddle.fluid.Scope()
scope = paddle.static.Scope()
exe.run(startup_program, scope=scope)
criterion = 'geometry_median'
pruner = Pruner(criterion)
......
tests/test_fsp_loss.py 已删除 100644 → 0
浏览文件 @ 87cfc2e6
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
sys.path.append("../")
import unittest
import paddle
from paddleslim.dist import merge, fsp
from layers import conv_bn_layer
from static_case import StaticCase
class TestFSPLoss(StaticCase):
def test_fsp_loss(self):
input = paddle.static.data(name="image", shape=[None, 3, 224, 224])
conv1 = conv_bn_layer(input, 8, 3, "conv1")
conv2 = conv_bn_layer(conv1, 8, 3, "conv2")
student_predict = conv1 + conv2
teacher_main = paddle.static.Program()
teacher_startup = paddle.static.Program()
with paddle.static.program_guard(teacher_main, teacher_startup):
input = paddle.static.data(name="image", shape=[None, 3, 224, 224])
conv1 = conv_bn_layer(input, 8, 3, "conv1")
conv2 = conv_bn_layer(conv1, 8, 3, "conv2")
sum1 = conv1 + conv2
conv3 = conv_bn_layer(sum1, 8, 3, "conv3")
conv4 = conv_bn_layer(conv3, 8, 3, "conv4")
sum2 = conv4 + sum1
conv5 = conv_bn_layer(sum2, 8, 3, "conv5")
teacher_predict = conv_bn_layer(conv5, 8, 3, "conv6")
place = paddle.CPUPlace()
data_name_map = {'image': 'image'}
merge(teacher_main,
paddle.static.default_main_program(), data_name_map, place)
merged_ops = []
for block in paddle.static.default_main_program().blocks:
for op in block.ops:
merged_ops.append(op.type)
distill_loss = fsp('teacher_conv1_out.tmp_1', 'teacher_conv6_out.tmp_0',
'conv1_out.tmp_0', 'conv2_out.tmp_0')
loss_ops = []
for block in paddle.static.default_main_program().blocks:
for op in block.ops:
loss_ops.append(op.type)
self.assertTrue(set(merged_ops).difference(set(loss_ops)) == set())
self.assertTrue(
set(loss_ops).difference(set(merged_ops)) ==
{'elementwise_sub', 'reduce_mean', 'square', 'fsp'})
if __name__ == '__main__':
unittest.main()
tests/test_optimal_threshold.py
浏览文件 @ 044909e7
......@@ -48,7 +48,7 @@ class TestPrune(StaticCase):
place = paddle.CPUPlace()
exe = paddle.static.Executor(place)
scope = paddle.fluid.Scope()
scope = paddle.static.Scope()
exe.run(startup_program, scope=scope)
criterion = 'bn_scale'
idx_selector = 'optimal_threshold'
......
tests/test_prune.py
浏览文件 @ 044909e7
......@@ -52,7 +52,7 @@ class TestPrune(StaticCase):
place = paddle.CPUPlace()
exe = paddle.static.Executor(place)
scope = paddle.fluid.Scope()
scope = paddle.static.Scope()
exe.run(startup_program, scope=scope)
pruner = Pruner()
main_program, _, _ = pruner.prune(
......
tests/test_prune_op.py
浏览文件 @ 044909e7
......@@ -44,7 +44,7 @@ class TestPrune(StaticCase):
place = paddle.CPUPlace()
exe = paddle.static.Executor(place)
scope = paddle.fluid.Scope()
scope = paddle.static.Scope()
exe.run(startup_program, scope=scope)
pruner = Pruner()
# test backward search of concat
......@@ -122,7 +122,7 @@ class TestSplit(StaticCase):
place = paddle.CPUPlace()
exe = paddle.static.Executor(place)
scope = paddle.fluid.Scope()
scope = paddle.static.Scope()
exe.run(startup_program, scope=scope)
pruner = Pruner()
# test backward search of concat
......@@ -159,7 +159,7 @@ class TestMul(StaticCase):
place = paddle.CPUPlace()
exe = paddle.static.Executor(place)
scope = paddle.fluid.Scope()
scope = paddle.static.Scope()
exe.run(startup_program, scope=scope)
pruner = Pruner()
# test backward search of concat
......
tests/test_prune_walker.py
浏览文件 @ 044909e7
......@@ -68,7 +68,7 @@ class TestPrune(StaticCase):
return cond_conv2
cond_output = paddle.static.nn.cond(cond, cond_block1, cond_block2)
sum3 = paddle.fluid.layers.sum([sum2, cond_output])
sum3 = paddle.add_n([sum2, cond_output])
conv6 = conv_bn_layer(sum3, 8, 3, "conv6")
sub1 = conv6 - sum3
......
tests/test_quant_aware.py
浏览文件 @ 044909e7
......@@ -39,7 +39,7 @@ class TestQuantAwareCase1(StaticCase):
def get_op_number(self, prog):
graph = paddle.fluid.framework.IrGraph(
paddle.fluid.core.Graph(prog.desc), for_test=False)
paddle.framework.core.Graph(prog.desc), for_test=False)
quant_op_nums = 0
op_nums = 0
for op in graph.all_op_nodes():
......
tests/test_slim_prune.py
浏览文件 @ 044909e7
......@@ -48,7 +48,7 @@ class TestPrune(StaticCase):
place = paddle.CPUPlace()
exe = paddle.static.Executor(place)
scope = paddle.fluid.Scope()
scope = paddle.static.Scope()
exe.run(startup_program, scope=scope)
criterion = 'bn_scale'
pruner = Pruner(criterion)
......
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